PatientProfiler is an R package that allows personalized analysis of an entire cohort and specific individuals. The main functions can be used sequentially or separately.
If we want to imagine a sequential way to use PatientProfiler, we can divide the pipeline into 4 main steps:
STEP 1 - Raw data harmonization / Access to harmonized CPTAC data and Input tables preparation
STEP 2 - Protein activity inference
STEP 3 - Generation of mechanistic models
STEP 4 - Network-based stratification: communities detection
STEP 5 - Identification of biomarkers
In this vignette, we describe how to access pre-harmonized CPTAC data to use them in PatientProfiler pipeline.
library(PatientProfiler)
library(SignalingProfiler)
library(tidyverse)If you want to use pre-harmonized data for running PatientProfiler, you can access them using the following function. You can specify one or a set of tumor types. start with this function of PatientProfiler. Our processed database includes transcritpomics, proteomics and phosphoproteomics data for 10 different tumors:
Brca - Breast cancer
Ccrcc - Clear cell renal cell carcinoma
Coad - Colon adenocarcinoma
Gbm - Glioblastoma
Hnscc -Head and neck squamous cell cacinoma
Lscc - Lung squamous cell carcinoma
Luad - Lung cancer
Ov - Ovarian cancer
Pdac - Pancreatic ductal adenocarcinoma
Ucec - Uterine corpus endometrial carcinoma
All the data has been harmonized using [omics_update] function. The function returns an RDS file containing CPTAC pre-harmonized data for user-selected tumor types.
If you need to extract Brca and Ov phosphoproteomics and proteomics data:
access_harmonized_CPTAC_data(tumors = c("Brca", "Ov"), omics = c("phospho", "prot"))The output is a local variable for each data frame you acceded.
You can follow the main vignette to use the CPTAC pre-harmonized data and generate patient-specific networks.